Random survival forests, an ensemble method for analysing right censored data, first introduced by Ishwaran et al, 2008. RSF has several advantages over Cox regression: (i) Unlike Cox regression, RSF does not rely on proportional hazard assumption. (ii) RSF accounts for nonlinear effects and interactions for factor variables.
A random survival forests analysis can be conducted by applying the following steps:
Random Survival Forests from Analysis tab.survival time, status variable, category value for status variable, and categorical and continuous predictors for the model.interaction terms, strata terms and time dependent covariates can be added to the model. Moreover, if there are multiple records for observations, users can specify it by clicking Multiple ID checkbox. From RSF options, number of tree, bootstrap method, randomly selected number of variable, minimum number of cases in terminal node, maximum depth for a tree, splitting rule, number of split, missing values, number of iterations of the missing data algorithm, proximity of cases, size of bootstrap and type of bootstrap can be adjusted.Run button to run the analysis.##
## Trees Grown: 710, Time Remaining (sec): 1
Survival predictions for each observation can be obtained. In this table, rows represent observations whereas columns represent time endpoints.
Out of bag (OOB) survival predictions for each observation can be obtained. In this table, rows represent observations whereas columns represent time endpoints.
Cumulative hazard predictions for each observation can be obtained. In this table, rows represent observations whereas columns represent time endpoints.
Out of bag (OOB) cumulative hazard predictions for each observation can be obtained. In this table, rows represent observations whereas columns represent time endpoints.
An error rate table, which shows error rate estimations for each tree, can be obtained.
A variable importance table as well as an interactive plot, which shows relative importance of variables in fitted model, can be obtained.
A survival plot can be created based on Nelson-Aalen estimator and overall ensemble predictions.
A survival plot can be drawn for survival predictions from random survival forests model. Each line represents a survival curve for each observation.
A survival plot can be drawn for OOB survival predictions from random survival forests model. Each line represents a survival curve for each observation.
A cumulative hazard plot can be drawn for hazard predictions from random survival forests model. Each line represents a survival curve for each observation.
A cumulative hazard plot can be drawn for OOB cumulative hazard predictions from random survival forests model. Each line represents a survival curve for each observation.
An interactive error rate plot, which shows error rate alterations when number of trees increased, can be drawn.
A Cox model can be compared to random survival forests model through an interactive plot for visual inspection of both models.
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## Trees Grown: 664, Time Remaining (sec): 2